2022/04/01 by Berrak Özer, Martin A. Karlsen, Özer, Berrak +23
Computer Science · Physics and Astronomy · #Advanced Image and Video Retrieval Techniques #Artificial intelligence #Computer science #Data mining #Database #Digital Libraries (cs.DL) #FOS: Computer and information sciences #FOS: Physical sciences #Handwritten Text Recognition Techniques #Identifier #Image (mathematics) #Image Retrieval and Classification Techniques #Information retrieval #Materials Science (cond-mat.mtrl-sci) #Object (grammar) #Programming language #Rank (graph theory) #Set (abstract data type) #Similarity (geometry) #Upload #World Wide Web #cond-mat.mtrl-sci #cs.DL
paper · pdf · doi:10.48550/arxiv.2204.00434
27 pages, 4 figures
arxiv created 2022/04/01 · openalex publication_date 2022/04/01 · arxiv updated 2022/04/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
We investigate a prototype application for machine-readable literature. The program is called "pyDataRecognition" and serves as an example of a data-driven literature search, where the literature search query is an experimental data-set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier (doi) and full reference of top ranked papers together with a stack plot of the user data alongside the top five database entries. The paper describes the approach and explores successes and challenges.